Methods › Computer Vision › Image Inpainting Modules › Contextual Residual Aggregation
Contextual Residual Aggregation
Introduced by Zili Yi et al. in Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Contextual Residual Aggregation, or CRA, is a module for image inpainting. It can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a low-resolution inpainted result and up-sample it to yield a large blurry image. Then we produce the high-frequency residuals for in-hole patches by aggregating weighted high-frequency residuals from contextual patches. Finally, we add the aggregated residuals to the large blurry image to obtain a sharp result.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting 19 May 2020 · 6 repositories · arXiv:2005.09704Syntology ran 2 of 9 samples · 7 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 2k | 1 |
| 8k | 1 |
| GPU | 1 |
| Image Inpainting | 1 |
| Vocal Bursts Intensity Prediction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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